Update app.py

app.py

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+import streamlit as st
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+from PIL import Image
+import json
+
+# Load the model
+model = tf.keras.models.load_model("satellite_image_model.h5")
+
+# Load class labels
+with open("label_map.json", "r") as f:
+    class_labels = json.load(f)
+
+# Reverse the class labels to get index to class name mapping
+class_labels = {v: k for k, v in class_labels.items()}
+
+# Function to preprocess the image
+def preprocess_image(image, target_size=(224, 224)):
+    image = image.resize(target_size)
+    image = img_to_array(image)
+    image = np.expand_dims(image, axis=0)
+    image = image / 255.0  # Normalizing
+    return image
+
+# Streamlit App
+st.title("Satellite Image Classification")
+
+st.write("Upload a satellite image, and the model will predict its class.")
+
+# Image uploader
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    
+    # Preprocess the image
+    processed_image = preprocess_image(image)
+    
+    # Make prediction
+    predictions = model.predict(processed_image)
+    class_idx = np.argmax(predictions, axis=1)[0]
+    predicted_class = class_labels[class_idx]
+    
+    # Show the predicted class
+    st.write(f"Predicted Class: {predicted_class}")